TY - JOUR
T1 - Identification of aquifer dispersivities in two‐dimensional transient groundwater Contaminant transport
T2 - An optimization approach
AU - Umari, Amjad
AU - Willis, Robert
AU - Liu, Philip L.‐F
PY - 1979/8
Y1 - 1979/8
N2 - The problem of identifying unknown aquifer dispersivities in two‐dimensional transient groundwater contaminant transport from given observations on the concentration field is addressed. This inverse problem is formulated as a general nonlinear programing problem, the purpose of which is to minimize the discrepancy between calculated and observed values of the concentration field. The method of quasilinearization is used to linearize the above problem, and the inverse algorithm becomes the solution of a sequence of linear programs that converge to the solution of the original nonlinear problem. The finite elements method in conjunction with finite differencing is used to discretize the governing differential equations which are then used as constraints for the optimization (mathematical programing) problem stated above. The effect on the inverse problem of the choice of observation points, objective function, number of finite elements, size of time step, and observation errors is studied. The proposed identification algorithm is shown to be fast, stable, and accurate.
AB - The problem of identifying unknown aquifer dispersivities in two‐dimensional transient groundwater contaminant transport from given observations on the concentration field is addressed. This inverse problem is formulated as a general nonlinear programing problem, the purpose of which is to minimize the discrepancy between calculated and observed values of the concentration field. The method of quasilinearization is used to linearize the above problem, and the inverse algorithm becomes the solution of a sequence of linear programs that converge to the solution of the original nonlinear problem. The finite elements method in conjunction with finite differencing is used to discretize the governing differential equations which are then used as constraints for the optimization (mathematical programing) problem stated above. The effect on the inverse problem of the choice of observation points, objective function, number of finite elements, size of time step, and observation errors is studied. The proposed identification algorithm is shown to be fast, stable, and accurate.
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U2 - 10.1029/WR015i004p00815
DO - 10.1029/WR015i004p00815
M3 - Article
AN - SCOPUS:0018655001
SN - 0043-1397
VL - 15
SP - 815
EP - 831
JO - Water Resources Research
JF - Water Resources Research
IS - 4
ER -